SoroushVahidi/scidocs huggingface dataset:
A Call for the Integration of Trauma-Informed Care Among Intellectual and Developmental Disability Organizations
(2014)

Research exploring the occurrence of trauma among adults with intellectual and developmental disabilities (I/DD) has grown over the past decade. Yet there is a dearth of literature investigating the impact of organizational factors on the trauma experience despite this population’s need for organizational supports. Trauma-informed care (TIC), a systems-focused model for service delivery, is a fast-developing interest among the broader field of trauma in the general population. It recognizes the prevalence and impact of trauma, and creates a culture of safety, trustworthiness, choice, collaboration, and empowerment. The author synthesized relevant literature from both the intellectual and developmental disabilities areas and integrated this with TIC and trauma literature drawn from the general population. Explored are the implications of organizations for service delivery and the potential assimilation of TIC within I/DD organizations. The effectiveness of TIC applications and their potential barriers are discussed and related to the philosophy of quality of life and organizational culture. The author notes that some individuals with I/DD comprise a vulnerable subgroup of the population that in large part relies upon the support of organizational services to foster quality of life. Given the implications of the focus on quality of life, he posits that TIC presents as a viable response for organizations, complimenting and augmenting current efforts.
Sea Ice Remote Sensing Using AMSR-E 89 GHz Channels
(2007)

Recent progress in sea ice concentration remote sensing by satellite microwave radiometers has been stimulated by two developments: First, the new sensor AMSR-E offers spatial resolutions of approximately 6x4 km at 89 GHz, nearly three times the resolution of the standard sensor SSM/I at 85 GHz (15x13 km). Second, a new algorithm enables to estimate sea ice concentration from the channels near 90GHz, despite the enhanced atmospheric influence in these channels. This allows to fully exploit their horizontal resolution which is up to four times finer than the one of the channels near 19 and 37 GHz, the frequencies used by the most widespread algorithms for sea ice retrieval, the NASA-Team and Bootstrap algorithms. The ASI algorithm used combines a model for retrieving the sea ice concentration from SSM/I 85 GHz data proposed by Svendsen et al. [1987] with an ocean mask derived from the 18-, 23-, and 37-GHz AMSR-E data using weather filters. During two ship campaigns, the correlation of ASI, NASA-Team 2 and Bootstrap algorithms ice concentrations with bridge observations were 0.80, 0.79 and 0.81, respectively. Systematic differences over the complete AMSR-E period (2002-2006) between ASI and NASA-Team 2 are below −2±8.8%, and between ASI and Bootstrap 1.7±10.8%. Among the geophysical implications of the ASI algorithm are: (1) Its higher spatial resolution allows to better estimate crucial variables in numerical atmospheric and ocean models, e.g. the heat flux between ocean and atmosphere, especially near coastlines and in polynyas. (2) It provides an additional time series of ice area and extent for climate studies.
Aware LSTM model for Semantic Role Labeling
(2017)

In Semantic Role Labeling (SRL) task, the tree structured dependency relation is rich in syntax information, but it is not well handled by existing models. In this paper, we propose Syntax Aware Long Short Time Memory (SA-LSTM). The structure of SA-LSTM changes according to dependency structure of each sentence, so that SA-LSTM can model the whole tree structure of dependency relation in an architecture engineering way. Experiments demonstrate that on Chinese Proposition Bank (CPB) 1.0, SA-LSTM improves F1 by 2.06% than ordinary bi-LSTM with feature engineered dependency relation information, and gives state-of-the-art F1 of 79.92%. On English CoNLL 2005 dataset, SA-LSTM brings improvement (2.1%) to bi-LSTM model and also brings slight improvement (0.3%) when added to the stateof-the-art model.
Adaptation algorithm for adaptive streaming over HTTP
(2012)

Internet video makes up a significant part of the Internet traffic and its fraction is constantly growing. In order to guarantee best user experience throughout different network access technologies with dynamically varying network conditions, it is fundamental to adopt technologies enabling a proper delivery of the media content. One of such technologies is adaptive streaming. It allows to dynamically adapt the bit-rate of the stream to varying network conditions. There are various approaches to adaptive streaming. In our work, we focus on the receiver-driven approach where the media file is subdivided into segments, each of the segments is provided at multiple bit-rates, and the task of the client is to select the appropriate bit-rate for each of the segments. With this approach, the challenges are (i) to properly estimate the dynamics of the available network throughput, (ii) to control the filling level of the client buffer in order to avoid underflows resulting in playback interruptions, (iii) to maximize the quality of the stream, while avoiding unnecessary quality fluctuations, and, finally, (iv) to minimize the delay between the user's request and the start of the playback. During our work, we designed and implemented a receiver-driven adaptation algorithm for adaptive streaming that does not rely on cross-layer information or server assistance. We integrated the algorithm with a prototype implementation of a streaming client based on the MPEG DASH (Dynamic Adaptive Streaming over HTTP) standard. We evaluated the implemented prototype in real-world scenarios and found that it performes remarkably well even under challenging network conditions. Further, it exhibits stable and fair operation if a common link is shared among multiple clients.
User Acceptance of Hedonic Information Technologies: A Perceived Value Perspective
(2007)

Hedonic information technologies do not focus on productivity-oriented applications, but rather, on lifestyle-augmentation and entertainment. As such users of hedonic IT are provided with benefits related to general enjoyment as opposed to gains in organizational efficiency. The main purpose of this study is to investigate this burgeoning phenomenon by measuring several value drivers for downloading mobile phone ringtones. Hypothesis testing was performed with PLS techniques applied to data collected from 119 ringtone users. Results confirmed that the overall value of hedonic IT is a third-order construct, which successfully predicted behavioral usage intentions and positive word of mouth.
Continuous monitoring of distance-based outliers over data streams
(2011)

Anomaly detection is considered an important data mining task, aiming at the discovery of elements (also known as outliers) that show significant diversion from the expected case. More specifically, given a set of objects the problem is to return the suspicious objects that deviate significantly from the typical behavior. As in the case of clustering, the application of different criteria lead to different definitions for an outlier. In this work, we focus on distance-based outliers: an object x is an outlier if there are less than k objects lying at distance at most R from x. The problem offers significant challenges when a stream-based environment is considered, where data arrive continuously and outliers must be detected on-the-fly. There are a few research works studying the problem of continuous outlier detection. However, none of these proposals meets the requirements of modern stream-based applications for the following reasons: (i) they demand a significant storage overhead, (ii) their efficiency is limited and (iii) they lack flexibility. In this work, we propose new algorithms for continuous outlier monitoring in data streams, based on sliding windows. Our techniques are able to reduce the required storage overhead, run faster than previously proposed techniques and offer significant flexibility. Experiments performed on real-life as well as synthetic data sets verify our theoretical study.
From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning
(2016)

We present a model of visually-grounded language learning based on stacked gated recurrent neural networks which learns to predict visual features given an image description in the form of a sequence of phonemes. The learning task resembles that faced by human language learners who need to discover both structure and meaning from noisy and ambiguous data across modalities. We show that our model indeed learns to predict features of the visual context given phonetically transcribed image descriptions, and show that it represents linguistic information in a hierarchy of levels: lower layers in the stack are comparatively more sensitive to form, whereas higher layers are more sensitive to meaning.
Electronic model of human brain using verilog
(2016)

Computers are man-made machines which work according to the given set of inputs and perform some operation on the input to generate a new set of outputs. They can be programmed to perform huge and complex task yet they lack imagination and ability to understand things. On the hand human brain is a machine which can learn new tasks by process of acquiring knowledge and understanding through thought, experience and the senses. To make computer work or think like human, human brain modeling of machine is necessary. We need to know how human brain works. The basic component of human brain is neuron, which make us think and do smart things. Brains have billions of neurons and they communicate with each other using electrical impulse. These impulses are responsible for making brain think and to have a consciousness. This paper proposes such a model of a part of the brain.
Synchronous reluctance motor multi-static MEC model
(2015)

High efficiency, low cost and high reliable electrical machines are designed to meet very demanding specifications for a wide range of applications. The SynchRM (Synchronous Reluctance Motor) has very interesting properties and features. One of the main advantages is that the motor can be designed without rare earth materials permanent magnets. In this article, a modelling method of such machines using the MEC (Magnetic Equivalent Circuit) is proposed. The main challenges are to obtain a very accurate parametric model in order to calculate torque variations and to make possible geometric optimizations in future work.
Learning Pixel-Level Semantic Affinity with Image-Level Supervision for Weakly Supervised Semantic Segmentation
(2018)

The deficiency of segmentation labels is one of the main obstacles to semantic segmentation in the wild. To alleviate this issue, we present a novel framework that generates segmentation labels of images given their image-level class labels. In this weakly supervised setting, trained models have been known to segment local discriminative parts rather than the entire object area. Our solution is to propagate such local responses to nearby areas which belong to the same semantic entity. To this end, we propose a Deep Neural Network (DNN) called AffinityNet that predicts semantic affinity between a pair of adjacent image coordinates. The semantic propagation is then realized by random walk with the affinities predicted by AffinityNet. More importantly, the supervision employed to train AffinityNet is given by the initial discriminative part segmentation, which is incomplete as a segmentation annotation but sufficient for learning semantic affinities within small image areas. Thus the entire framework relies only on image-level class labels and does not require any extra data or annotations. On the PASCAL VOC 2012 dataset, a DNN learned with segmentation labels generated by our method outperforms previous models trained with the same level of supervision, and is even as competitive as those relying on stronger supervision.
Generalized distributed rate limiting
(2009)

The Distributed Rate Limiting (DRL) paradigm is a recently proposed mechanism for decentralized control of cloud-based services. DRL is a simple and efficient approach to resolve the issues of pricing and resource control/engineering of cloud based services. The existing DRL schemes focus on very specific performance metrics (such as loss rate and fair-share) and their design heavily depends on the assumption that the traffic is generated by elastic TCP sources. In this paper we tackle the DRL problem for general workloads and performance metrics and propose an analytic framework for the design of stable DRL algorithms. The closed-form nature of our results allows simple design rules which, together with extremely low communication overhead, makes the presented algorithms practical and easy to deploy with guaranteed convergence properties under a wide range of possible scenarios.
1 Wave Dispersion in High-Rise Buildings due to Soil-Structure Interaction
(2014)

Nonparametric techniques for estimation of wave dispersion in buildings by seismic interferometry are applied to a simple model of a soil-structure interaction (SSI) system with coupled horizontal and rocking response. The system consists of a viscously damped shearbeam, representing a building, on a rigid foundation embedded in a half-space. The analysis shows that (1) wave propagation through the system is dispersive. The dispersion is characterized by lower phase velocity (softening) in the band containing the fundamental system mode of vibration, and little change in the higher frequency bands, relative to the building shear wave velocity. This mirrors its well known effect on the frequencies of vibration, i.e. reduction for the fundamental mode (softening) and no significant change for the higher modes of vibration, in agreement with the duality of the wave and vibrational nature of structural response. Nevertheless, the phase velocity identified from broader band IRFs is very close to the superstructure shear wave velocity, as found by the earlier study. The analysis reveals that (2) the reason for this apparent paradox is that the latter estimates are biased towards the higher values, representative of the higher frequencies in the band, where the response is less affected by SSI. It is also discussed that (3) bending flexibility and soil flexibility produce similar effects on the phase velocities and frequencies of vibration of a building. 1 Ph.D. Candidate, University of Southern California, Dept. of Civil Eng., Los Angeles, CA 90089-2531, Email: mrahmani@usc.edu 2 Ph.D. Candidate, University of Southern California, Dept. of Civil Eng., Los Angeles, CA 90089-2531, Email: mebrahim@usc.edu 3 Research Professor, University of Southern California, Dept. of Civil Eng., Los Angeles, CA 90089-2531, Email: mtodorov@usc.edu Earthquake Engineering and Structural Dynamics. DOI: 10.1002/eqe.2454, Final Draft. First published online on June 23, 2014, in press Article available at: http://onlinelibrary.wiley.com/doi/10.1002/eqe.2454/abstract.
Clinical anatomy of the coccyx: A systematic review.
(2012)

The coccyx has been relatively neglected in anatomical research which is surprising given the population prevalence of coccydynia and our inadequate understanding of its etiology. This systematic review analyzes available information on the clinical anatomy of the coccyx. A literature search using five electronic databases and standard anatomy reference texts was conducted yielding 61 primary and 7 secondary English-language sources. This was supplemented by a manual search of selected historical foreign language articles. The coccygeal vertebrae, associated joints, ligaments and muscles, coccygeal movements, nerves, and blood supply were analyzed in detail. Although the musculoskeletal aspects of the coccyx are reasonably well described, the precise anatomy of the coccygeal plexus and its distribution, the function of the coccygeal body, and the anatomy of the sacrococcygeal zygapophyseal joints are poorly documented. Further research into the anatomy of the coccyx may clarify the etiopathogenesis of coccydynia which remains uncertain in one-third of affected patients.
ASSESSMENT OF SLOPE STABILITY IN EMBANKMENT DAMS USING ARTIFICIAL NEURAL NETWORK
(2012)

A slope formed when there is a height difference between two points of the ground surface. Limited or ambiguous data defined in the studying of the slip surface have always been posed as a challenge to engineers and geologists; however, previous studies presented the useful strategies for resolving this challenge. In general, Multi Layer Perceptrons (MLPs) (with three input, output and hidden layers) artificial neural networks (ANNs), as well as the transfer function and sufficient number of neurons in hidden layer are able to estimate any nonlinear function with any degree of complexity. In the present study, the Zonouz embankment dam at Iran was firstly modeled by using the classic limit equilibrium method (LEM) then, obtained results were used for training the ANN. Then the effect of various parameters such as logarithmic data and training epoch number on the network and sensitivity analysis on the input parameters were discussed. Finally the best network was selected in training step, and obtained results were verified. The results show that the ANN can be used as a suitable alternative technique instead of LEM for probing the slope stability in embankment dams.
Improving SAT-solving with Machine Learning
(2017)

In this project, we aimed to improve the runtime of Minisat, a Conflict-Driven Clause Learning (CDCL) solver that solves the Propositional Boolean Satisfiability (SAT) problem. We first used a logistic regression model to predict the satisfiability of propositional boolean formulae after fixing the values of a certain fraction of the variables in each formula. We then applied the logistic model and added a preprocessing period to Minisat to determine the preferable initial value (either true or false) of each boolean variable using a Monte-Carlo approach. Concretely, for each Monte-Carlo trial, we fixed the values of a certain ratio of randomly selected variables, and calculated the confidence that the resulting sub-formula is satisfiable with our logistic regression model. The initial value of each variable was set based on the mean confidence scores of the trials that started from the literals of that variable. We were particularly interested in setting the initial values of the backbone variables correctly, which are variables that have the same value in all solutions of a SAT formula. Our Monte-Carlo method was able to set 78% of the backbones correctly. Excluding the preprocessing time, compared with the default setting of Minisat, the runtime of Minisat for satisfiable formulae decreased by 23%. However, our method did not outperform vanilla Minisat in runtime, as the decrease in the conflicts was outweighed by the long runtime of the preprocessing period.
Semantic Models for Scalable Search in the Internet of Things
(2013)

The Internet of Things is anticipated to connect billions of embedded devices equipped with sensors to perceive their surroundings. Thereby, the state of the real world will be available online and in real-time and can be combined with other data and services in the Internet to realize novel applications such as Smart Cities, Smart Grids, or Smart Healthcare. This requires an open representation of sensor data and scalable search over data from diverse sources including sensors. In this paper we show how the Semantic Web technologies RDF (an open semantic data format) and SPARQL (a query language for RDF-encoded data) can be used to address those challenges. In particular, we describe how prediction models can be employed for scalable sensor search, how these prediction models can be encoded as RDF, and how the models can be queried by means of SPARQL.
Analyzing the Performance of Multilayer Neural Networks for Object Recognition
(2014)

In the last two years, convolutional neural networks (CNNs) have achieved an impressive suite of results on standard recognition datasets and tasks. CNN-based features seem poised to quickly replace engineered representations, such as SIFT and HOG. However, compared to SIFT and HOG, we understand much less about the nature of the features learned by large CNNs. In this paper, we experimentally probe several aspects of CNN feature learning in an attempt to help practitioners gain useful, evidence-backed intuitions about how to apply CNNs to computer vision problems.
SenGen: Sentence Generating Neural Variational Topic Model
(2017)

We present a new topic model that generates documents by sampling a topic for one whole sentence at a time, and generating the words in the sentence using an RNN decoder that is conditioned on the topic of the sentence. We argue that this novel formalism will help us not only visualize and model the topical discourse structure in a document better, but also potentially lead to more interpretable topics since we can now illustrate topics by sampling representative sentences instead of bag of words or phrases. We present a variational auto-encoder approach for learning in which we use a factorized variational encoder that independently models the posterior over topical mixture vectors of documents using a feedforward network, and the posterior over topic assignments to sentences using an RNN. Our preliminary experiments on two different datasets indicate early promise, but also expose many challenges that remain to be addressed.
Understanding packet delivery performance in dense wireless sensor networks
(2003)

Wireless sensor networks promise fine-grain monitoring in a wide variety of environments. Many of these environments (e.g., indoor environments or habitats) can be harsh for wireless communication. From a networking perspective, the most basic aspect of wireless communication is the packet delivery performance: the spatio-temporal characteristics of packet loss, and its environmental dependence. These factors will deeply impact the performance of data acquisition from these networks.In this paper, we report on a systematic medium-scale (up to sixty nodes) measurement of packet delivery in three different environments: an indoor office building, a habitat with moderate foliage, and an open parking lot. Our findings have interesting implications for the design and evaluation of routing and medium-access protocols for sensor networks.